# spatial-kfold
spatial resampling for more robust cross validation in spatial studies
spatial-kfold is a python library for performing spatial resampling to ensure more robust cross-validation in spatial studies. It offers spatial clustering and block resampling technique with user-friendly parameters to customize the resampling. It enables users to conduct a "Leave Region Out" cross-validation, which can be useful for evaluating the model's generalization to new locations as well as improving the reliability of [feature selection](https://doi.org/10.1016/j.ecolmodel.2019.108815) and [hyperparameter tuning](https://doi.org/10.1016/j.ecolmodel.2019.06.002) in spatial studies
Raw data
{
"_id": null,
"home_page": "https://github.com/WalidGharianiEAGLE/spatial-moto",
"name": "spatial-moto",
"maintainer": "",
"docs_url": null,
"requires_python": ">=3.7",
"maintainer_email": "",
"keywords": "spatial",
"author": "Walid Ghariani",
"author_email": "",
"download_url": "https://files.pythonhosted.org/packages/65/c3/d4493e569f3693e523eac23b222ff7e17c67184d2658f4dd25979557b92a/spatial-moto-0.0.6.tar.gz",
"platform": null,
"description": "# spatial-kfold\nspatial resampling for more robust cross validation in spatial studies\n\nspatial-kfold is a python library for performing spatial resampling to ensure more robust cross-validation in spatial studies. It offers spatial clustering and block resampling technique with user-friendly parameters to customize the resampling. It enables users to conduct a \"Leave Region Out\" cross-validation, which can be useful for evaluating the model's generalization to new locations as well as improving the reliability of [feature selection](https://doi.org/10.1016/j.ecolmodel.2019.108815) and [hyperparameter tuning](https://doi.org/10.1016/j.ecolmodel.2019.06.002) in spatial studies\n",
"bugtrack_url": null,
"license": "MIT",
"summary": "spatial-moto: test 1",
"version": "0.0.6",
"project_urls": {
"Homepage": "https://github.com/WalidGharianiEAGLE/spatial-moto"
},
"split_keywords": [
"spatial"
],
"urls": [
{
"comment_text": "",
"digests": {
"blake2b_256": "27116bcc939e95dd864cc49a0d69ce6a5f5aab489d3181229091a3a157762a88",
"md5": "3a2d184963acf1348e476566d9e828dc",
"sha256": "e7c63e7c0cfc7360f3b2956481e9e0c4488652cc2262d76fea4c060a29744691"
},
"downloads": -1,
"filename": "spatial_moto-0.0.6-py3-none-any.whl",
"has_sig": false,
"md5_digest": "3a2d184963acf1348e476566d9e828dc",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": ">=3.7",
"size": 273315,
"upload_time": "2023-06-14T13:21:55",
"upload_time_iso_8601": "2023-06-14T13:21:55.825910Z",
"url": "https://files.pythonhosted.org/packages/27/11/6bcc939e95dd864cc49a0d69ce6a5f5aab489d3181229091a3a157762a88/spatial_moto-0.0.6-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "65c3d4493e569f3693e523eac23b222ff7e17c67184d2658f4dd25979557b92a",
"md5": "2448536dbf743730006808d8e8380137",
"sha256": "a1f6d93f21b556f62bbff67fb6987d46dddaf6c9bf11dbd4c8fa62dc90c725cd"
},
"downloads": -1,
"filename": "spatial-moto-0.0.6.tar.gz",
"has_sig": false,
"md5_digest": "2448536dbf743730006808d8e8380137",
"packagetype": "sdist",
"python_version": "source",
"requires_python": ">=3.7",
"size": 246261,
"upload_time": "2023-06-14T13:21:59",
"upload_time_iso_8601": "2023-06-14T13:21:59.779370Z",
"url": "https://files.pythonhosted.org/packages/65/c3/d4493e569f3693e523eac23b222ff7e17c67184d2658f4dd25979557b92a/spatial-moto-0.0.6.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2023-06-14 13:21:59",
"github": true,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"github_user": "WalidGharianiEAGLE",
"github_project": "spatial-moto",
"travis_ci": false,
"coveralls": false,
"github_actions": false,
"requirements": [],
"lcname": "spatial-moto"
}